Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:05, 1.86MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:05<00:00, 10.6KFile/s]
Downloading celeba: 1.44GB [02:39, 9.07MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fdd82bbde80>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fdd82e5b860>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input_image = tf.placeholder(tf.float32, shape=[None, image_width, image_height, image_channels], name = "Real_Input")
    z_data = tf.placeholder(tf.float32, shape=[None, z_dim], name = "Z_Input")
    lr = tf.placeholder(tf.float32, shape=[], name = "Learning_Rate")

    return real_input_image, z_data, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def LReLU(logits, alpha=0.2):
    return tf.maximum(logits*alpha, logits)

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        conv1 = tf.layers.conv2d(inputs=images, filters=64, kernel_size=(3,3),
                                  strides=(2,2), padding='same')
        lrelu1 = LReLU(conv1)     

        conv2 = tf.layers.conv2d(inputs=lrelu1, filters=128, kernel_size=(3,3),
                                  strides=(2,2), padding='same')
        bnorm1 = tf.layers.batch_normalization(conv2, training=True)
        lrelu2 = LReLU(bnorm1)
        
        conv3 = tf.layers.conv2d(inputs = lrelu2, filters=256, kernel_size=(3,3),
                                  strides=(2,2), padding='same')
        bnorm2 = tf.layers.batch_normalization(conv3, training=True)
        lrelu3 = LReLU(bnorm2)
        
        
        # 4th Convolutional Layer w/ LeakyReLU activation and batch norm
        conv4 = tf.layers.conv2d(inputs=lrelu3, filters=512, kernel_size=(3,3),
                                  strides=(2,2), padding='same')
        bnorm3 = tf.layers.batch_normalization(conv4, training=True)
        lrelu4 = LReLU(bnorm3)
        
        # Flattened Layer
        flat = tf.contrib.layers.flatten(lrelu4)
        
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse = not(is_train)):
        fc = tf.layers.dense(inputs = z, units = 2*2*512)
        fc = tf.reshape(fc, (-1, 2, 2, 512))
        fc = tf.layers.batch_normalization(fc, training=is_train)
        lrelu1 = LReLU(fc)
        
        conv1 = tf.layers.conv2d_transpose(inputs=lrelu1, filters=224, kernel_size=(3, 3), strides=(2, 2), padding='same')
        bnorm1 = tf.layers.batch_normalization(conv1, training=is_train)
        lrelu2 = LReLU(bnorm1)
        
        conv2 = tf.layers.conv2d_transpose(inputs=lrelu2, filters=112, kernel_size=(4, 4), strides=(1, 1), padding='valid')
        bnorm2 = tf.layers.batch_normalization(conv2, training=is_train)
        lrelu3 = LReLU(bnorm2)
        
        conv3 = tf.layers.conv2d_transpose(inputs=lrelu3, filters=56, kernel_size=(3, 3), strides=(2, 2), padding='same')
        bnorm3 = tf.layers.batch_normalization(conv3, training=is_train)
        lrelu4 = LReLU(bnorm3)

        conv4 = tf.layers.conv2d_transpose(inputs=lrelu4, filters=28, kernel_size=(5, 5), strides=(1, 1), padding='same')
        bnorm4 = tf.layers.batch_normalization(conv4, training=is_train)
        lrelu5 = LReLU(bnorm4)
        
        out = tf.layers.conv2d_transpose(inputs=lrelu5, filters=out_channel_dim, 
                                            kernel_size=(3, 3), strides=(2, 2),
                                            padding='same', activation = tf.tanh)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    (d_model_real, d_logits_real) = discriminator(input_real, reuse = False)
    (d_model_fake, d_logits_fake) = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.97))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) 
    with tf.control_dependencies([op for op in update_ops if op.name.startswith('generator')]):
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    image_channels = data_shape[3]
    (input_real, input_z, lr) = model_inputs(image_width=data_shape[1], image_height=data_shape[2],
                                            image_channels=image_channels,z_dim=z_dim)
    
    (d_loss, g_loss) = model_loss(input_real, input_z, image_channels)
    (d_train_opt, g_train_opt) = model_opt(d_loss, g_loss, learning_rate, beta1)
    (samples, losses) = [], []
    print_every = 100
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps += 1
                # TODO: Train Model
                batch_images *= 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, lr: learning_rate})
                if steps % 25 == 0:
                    show_generator_output(sess=sess, image_mode=data_image_mode,
                                          input_z=input_z, n_images=25,
                                          out_channel_dim=image_channels)
                    # get the losses and print them out
                    train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 32
z_dim = 50
learning_rate = 0.01
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 25 Discriminator Loss: 1.5575... Generator Loss: 0.5646
Epoch 1/2... Batch 50 Discriminator Loss: 1.3354... Generator Loss: 1.1026
Epoch 1/2... Batch 75 Discriminator Loss: 1.6359... Generator Loss: 0.3131
Epoch 1/2... Batch 100 Discriminator Loss: 1.2961... Generator Loss: 0.4225
Epoch 1/2... Batch 125 Discriminator Loss: 2.0070... Generator Loss: 0.2573
Epoch 1/2... Batch 150 Discriminator Loss: 1.2764... Generator Loss: 0.8241
Epoch 1/2... Batch 175 Discriminator Loss: 1.9074... Generator Loss: 0.4762
Epoch 1/2... Batch 200 Discriminator Loss: 1.5160... Generator Loss: 0.4092
Epoch 1/2... Batch 225 Discriminator Loss: 1.7092... Generator Loss: 0.2916
Epoch 1/2... Batch 250 Discriminator Loss: 1.5536... Generator Loss: 0.3428
Epoch 1/2... Batch 275 Discriminator Loss: 1.3052... Generator Loss: 1.6016
Epoch 1/2... Batch 300 Discriminator Loss: 1.3687... Generator Loss: 0.6291
Epoch 1/2... Batch 325 Discriminator Loss: 1.5757... Generator Loss: 0.5907
Epoch 1/2... Batch 350 Discriminator Loss: 1.3235... Generator Loss: 0.6829
Epoch 1/2... Batch 375 Discriminator Loss: 1.5673... Generator Loss: 0.7172
Epoch 1/2... Batch 400 Discriminator Loss: 1.5939... Generator Loss: 0.3606
Epoch 1/2... Batch 425 Discriminator Loss: 1.4249... Generator Loss: 0.6277
Epoch 1/2... Batch 450 Discriminator Loss: 1.5255... Generator Loss: 0.5359
Epoch 1/2... Batch 475 Discriminator Loss: 1.4017... Generator Loss: 0.4461
Epoch 1/2... Batch 500 Discriminator Loss: 1.2400... Generator Loss: 0.6441
Epoch 1/2... Batch 525 Discriminator Loss: 1.3942... Generator Loss: 1.2767
Epoch 1/2... Batch 550 Discriminator Loss: 1.3792... Generator Loss: 0.5939
Epoch 1/2... Batch 575 Discriminator Loss: 1.2727... Generator Loss: 0.7396
Epoch 1/2... Batch 600 Discriminator Loss: 1.1078... Generator Loss: 1.0768
Epoch 1/2... Batch 625 Discriminator Loss: 1.2588... Generator Loss: 0.6031
Epoch 1/2... Batch 650 Discriminator Loss: 1.1542... Generator Loss: 0.6922
Epoch 1/2... Batch 675 Discriminator Loss: 1.8582... Generator Loss: 0.2390
Epoch 1/2... Batch 700 Discriminator Loss: 1.3461... Generator Loss: 0.7674
Epoch 1/2... Batch 725 Discriminator Loss: 1.2287... Generator Loss: 0.9742
Epoch 1/2... Batch 750 Discriminator Loss: 1.3103... Generator Loss: 0.7401
Epoch 1/2... Batch 775 Discriminator Loss: 1.8117... Generator Loss: 0.2502
Epoch 1/2... Batch 800 Discriminator Loss: 1.7546... Generator Loss: 0.2915
Epoch 1/2... Batch 825 Discriminator Loss: 1.3350... Generator Loss: 0.7011
Epoch 1/2... Batch 850 Discriminator Loss: 1.2296... Generator Loss: 0.6063
Epoch 1/2... Batch 875 Discriminator Loss: 1.6065... Generator Loss: 0.3695
Epoch 1/2... Batch 900 Discriminator Loss: 1.7680... Generator Loss: 0.2625
Epoch 1/2... Batch 925 Discriminator Loss: 1.3981... Generator Loss: 0.6157
Epoch 1/2... Batch 950 Discriminator Loss: 1.6757... Generator Loss: 0.3604
Epoch 1/2... Batch 975 Discriminator Loss: 1.6009... Generator Loss: 0.4893
Epoch 1/2... Batch 1000 Discriminator Loss: 1.2754... Generator Loss: 0.6434
Epoch 1/2... Batch 1025 Discriminator Loss: 1.1892... Generator Loss: 0.6140
Epoch 1/2... Batch 1050 Discriminator Loss: 1.4359... Generator Loss: 0.7485
Epoch 1/2... Batch 1075 Discriminator Loss: 2.1330... Generator Loss: 0.1651
Epoch 1/2... Batch 1100 Discriminator Loss: 1.2982... Generator Loss: 0.7653
Epoch 1/2... Batch 1125 Discriminator Loss: 1.6224... Generator Loss: 0.7149
Epoch 1/2... Batch 1150 Discriminator Loss: 2.2714... Generator Loss: 0.1553
Epoch 1/2... Batch 1175 Discriminator Loss: 1.6879... Generator Loss: 0.2835
Epoch 1/2... Batch 1200 Discriminator Loss: 1.7362... Generator Loss: 0.3164
Epoch 1/2... Batch 1225 Discriminator Loss: 1.2220... Generator Loss: 0.5973
Epoch 1/2... Batch 1250 Discriminator Loss: 1.3920... Generator Loss: 1.4996
Epoch 1/2... Batch 1275 Discriminator Loss: 1.5989... Generator Loss: 0.4800
Epoch 1/2... Batch 1300 Discriminator Loss: 1.6099... Generator Loss: 0.6158
Epoch 1/2... Batch 1325 Discriminator Loss: 1.4218... Generator Loss: 0.7683
Epoch 1/2... Batch 1350 Discriminator Loss: 1.5027... Generator Loss: 1.0627
Epoch 1/2... Batch 1375 Discriminator Loss: 1.2838... Generator Loss: 0.6490
Epoch 1/2... Batch 1400 Discriminator Loss: 1.5483... Generator Loss: 0.3993
Epoch 1/2... Batch 1425 Discriminator Loss: 1.7500... Generator Loss: 0.3854
Epoch 1/2... Batch 1450 Discriminator Loss: 1.4499... Generator Loss: 0.5060
Epoch 1/2... Batch 1475 Discriminator Loss: 1.5800... Generator Loss: 0.4215
Epoch 1/2... Batch 1500 Discriminator Loss: 1.5277... Generator Loss: 0.4490
Epoch 1/2... Batch 1525 Discriminator Loss: 1.4704... Generator Loss: 0.3844
Epoch 1/2... Batch 1550 Discriminator Loss: 2.4199... Generator Loss: 0.1470
Epoch 1/2... Batch 1575 Discriminator Loss: 1.3552... Generator Loss: 0.8619
Epoch 1/2... Batch 1600 Discriminator Loss: 1.0517... Generator Loss: 0.8682
Epoch 1/2... Batch 1625 Discriminator Loss: 1.1513... Generator Loss: 1.4792
Epoch 1/2... Batch 1650 Discriminator Loss: 1.2184... Generator Loss: 0.8010
Epoch 1/2... Batch 1675 Discriminator Loss: 1.2341... Generator Loss: 0.5924
Epoch 1/2... Batch 1700 Discriminator Loss: 1.0582... Generator Loss: 0.9135
Epoch 1/2... Batch 1725 Discriminator Loss: 2.7335... Generator Loss: 0.1010
Epoch 1/2... Batch 1750 Discriminator Loss: 1.9591... Generator Loss: 0.2407
Epoch 1/2... Batch 1775 Discriminator Loss: 3.1134... Generator Loss: 0.0700
Epoch 1/2... Batch 1800 Discriminator Loss: 0.8816... Generator Loss: 1.6858
Epoch 1/2... Batch 1825 Discriminator Loss: 3.0229... Generator Loss: 1.0651
Epoch 1/2... Batch 1850 Discriminator Loss: 1.7566... Generator Loss: 0.2789
Epoch 1/2... Batch 1875 Discriminator Loss: 1.0542... Generator Loss: 0.8544
Epoch 2/2... Batch 25 Discriminator Loss: 1.4019... Generator Loss: 1.0008
Epoch 2/2... Batch 50 Discriminator Loss: 1.4460... Generator Loss: 0.3741
Epoch 2/2... Batch 75 Discriminator Loss: 1.1889... Generator Loss: 0.6113
Epoch 2/2... Batch 100 Discriminator Loss: 1.6143... Generator Loss: 0.3619
Epoch 2/2... Batch 125 Discriminator Loss: 1.0124... Generator Loss: 1.1079
Epoch 2/2... Batch 150 Discriminator Loss: 3.0139... Generator Loss: 0.0808
Epoch 2/2... Batch 175 Discriminator Loss: 1.4076... Generator Loss: 0.4259
Epoch 2/2... Batch 200 Discriminator Loss: 1.4575... Generator Loss: 0.4960
Epoch 2/2... Batch 225 Discriminator Loss: 1.3632... Generator Loss: 0.6116
Epoch 2/2... Batch 250 Discriminator Loss: 1.8123... Generator Loss: 0.2967
Epoch 2/2... Batch 275 Discriminator Loss: 2.0369... Generator Loss: 0.1963
Epoch 2/2... Batch 300 Discriminator Loss: 1.7299... Generator Loss: 0.3455
Epoch 2/2... Batch 325 Discriminator Loss: 2.1945... Generator Loss: 0.1742
Epoch 2/2... Batch 350 Discriminator Loss: 3.3908... Generator Loss: 0.0544
Epoch 2/2... Batch 375 Discriminator Loss: 1.7317... Generator Loss: 0.3378
Epoch 2/2... Batch 400 Discriminator Loss: 3.0242... Generator Loss: 0.0710
Epoch 2/2... Batch 425 Discriminator Loss: 3.3636... Generator Loss: 0.0495
Epoch 2/2... Batch 450 Discriminator Loss: 1.1578... Generator Loss: 0.5851
Epoch 2/2... Batch 475 Discriminator Loss: 0.9345... Generator Loss: 1.6227
Epoch 2/2... Batch 500 Discriminator Loss: 2.2209... Generator Loss: 0.1661
Epoch 2/2... Batch 525 Discriminator Loss: 1.7141... Generator Loss: 0.3631
Epoch 2/2... Batch 550 Discriminator Loss: 1.3688... Generator Loss: 0.8341
Epoch 2/2... Batch 575 Discriminator Loss: 1.6856... Generator Loss: 2.2883
Epoch 2/2... Batch 600 Discriminator Loss: 1.4800... Generator Loss: 0.6540
Epoch 2/2... Batch 625 Discriminator Loss: 2.9814... Generator Loss: 0.1138
Epoch 2/2... Batch 650 Discriminator Loss: 2.0006... Generator Loss: 0.2647
Epoch 2/2... Batch 675 Discriminator Loss: 1.2748... Generator Loss: 0.8204
Epoch 2/2... Batch 700 Discriminator Loss: 1.8928... Generator Loss: 0.2869
Epoch 2/2... Batch 725 Discriminator Loss: 1.2947... Generator Loss: 0.6364
Epoch 2/2... Batch 750 Discriminator Loss: 1.7249... Generator Loss: 0.2917
Epoch 2/2... Batch 775 Discriminator Loss: 2.2324... Generator Loss: 0.1489
Epoch 2/2... Batch 800 Discriminator Loss: 1.6082... Generator Loss: 0.4115
Epoch 2/2... Batch 825 Discriminator Loss: 1.0494... Generator Loss: 2.5280
Epoch 2/2... Batch 850 Discriminator Loss: 1.5165... Generator Loss: 0.4614
Epoch 2/2... Batch 875 Discriminator Loss: 2.2336... Generator Loss: 0.2930
Epoch 2/2... Batch 900 Discriminator Loss: 1.4502... Generator Loss: 0.5017
Epoch 2/2... Batch 925 Discriminator Loss: 0.6212... Generator Loss: 2.8496
Epoch 2/2... Batch 950 Discriminator Loss: 2.0218... Generator Loss: 1.1384
Epoch 2/2... Batch 975 Discriminator Loss: 0.9915... Generator Loss: 1.4956
Epoch 2/2... Batch 1000 Discriminator Loss: 0.9159... Generator Loss: 0.9733
Epoch 2/2... Batch 1025 Discriminator Loss: 1.9414... Generator Loss: 0.2322
Epoch 2/2... Batch 1050 Discriminator Loss: 2.8355... Generator Loss: 0.0900
Epoch 2/2... Batch 1075 Discriminator Loss: 2.2329... Generator Loss: 0.1620
Epoch 2/2... Batch 1100 Discriminator Loss: 1.6119... Generator Loss: 3.7296
Epoch 2/2... Batch 1125 Discriminator Loss: 0.9292... Generator Loss: 0.7661
Epoch 2/2... Batch 1150 Discriminator Loss: 3.3671... Generator Loss: 0.1020
Epoch 2/2... Batch 1175 Discriminator Loss: 3.1409... Generator Loss: 0.0719
Epoch 2/2... Batch 1200 Discriminator Loss: 1.8763... Generator Loss: 0.2887
Epoch 2/2... Batch 1225 Discriminator Loss: 2.8878... Generator Loss: 0.1262
Epoch 2/2... Batch 1250 Discriminator Loss: 2.9771... Generator Loss: 0.1123
Epoch 2/2... Batch 1275 Discriminator Loss: 1.7470... Generator Loss: 0.3289
Epoch 2/2... Batch 1300 Discriminator Loss: 1.3612... Generator Loss: 0.6519
Epoch 2/2... Batch 1325 Discriminator Loss: 2.3454... Generator Loss: 0.1667
Epoch 2/2... Batch 1350 Discriminator Loss: 2.0729... Generator Loss: 0.2137
Epoch 2/2... Batch 1375 Discriminator Loss: 1.2380... Generator Loss: 0.5443
Epoch 2/2... Batch 1400 Discriminator Loss: 1.2092... Generator Loss: 0.5485
Epoch 2/2... Batch 1425 Discriminator Loss: 2.6538... Generator Loss: 0.1248
Epoch 2/2... Batch 1450 Discriminator Loss: 0.9273... Generator Loss: 0.7930
Epoch 2/2... Batch 1475 Discriminator Loss: 2.0525... Generator Loss: 0.2235
Epoch 2/2... Batch 1500 Discriminator Loss: 3.3302... Generator Loss: 0.0871
Epoch 2/2... Batch 1525 Discriminator Loss: 1.7134... Generator Loss: 0.4852
Epoch 2/2... Batch 1550 Discriminator Loss: 1.2326... Generator Loss: 0.5833
Epoch 2/2... Batch 1575 Discriminator Loss: 2.0923... Generator Loss: 0.2529
Epoch 2/2... Batch 1600 Discriminator Loss: 1.6716... Generator Loss: 0.3686
Epoch 2/2... Batch 1625 Discriminator Loss: 2.7204... Generator Loss: 0.1325
Epoch 2/2... Batch 1650 Discriminator Loss: 2.3369... Generator Loss: 0.2041
Epoch 2/2... Batch 1675 Discriminator Loss: 2.6288... Generator Loss: 0.3385
Epoch 2/2... Batch 1700 Discriminator Loss: 2.3565... Generator Loss: 0.1740
Epoch 2/2... Batch 1725 Discriminator Loss: 1.4240... Generator Loss: 1.8851
Epoch 2/2... Batch 1750 Discriminator Loss: 2.4305... Generator Loss: 0.1406
Epoch 2/2... Batch 1775 Discriminator Loss: 3.1283... Generator Loss: 0.1152
Epoch 2/2... Batch 1800 Discriminator Loss: 2.1610... Generator Loss: 0.2422
Epoch 2/2... Batch 1825 Discriminator Loss: 3.1103... Generator Loss: 0.0759
Epoch 2/2... Batch 1850 Discriminator Loss: 2.8605... Generator Loss: 0.0810
Epoch 2/2... Batch 1875 Discriminator Loss: 3.7185... Generator Loss: 0.0498

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 32
z_dim = 50
learning_rate = 0.01
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 25 Discriminator Loss: 2.7799... Generator Loss: 2.0977
Epoch 1/1... Batch 50 Discriminator Loss: 1.4545... Generator Loss: 0.5268
Epoch 1/1... Batch 75 Discriminator Loss: 1.7965... Generator Loss: 1.2706
Epoch 1/1... Batch 100 Discriminator Loss: 2.0408... Generator Loss: 0.4201
Epoch 1/1... Batch 125 Discriminator Loss: 1.4253... Generator Loss: 0.7032
Epoch 1/1... Batch 150 Discriminator Loss: 1.3035... Generator Loss: 1.2043
Epoch 1/1... Batch 175 Discriminator Loss: 1.2187... Generator Loss: 0.6511
Epoch 1/1... Batch 200 Discriminator Loss: 1.4492... Generator Loss: 0.6681
Epoch 1/1... Batch 225 Discriminator Loss: 1.4531... Generator Loss: 1.0503
Epoch 1/1... Batch 250 Discriminator Loss: 1.4628... Generator Loss: 0.6661
Epoch 1/1... Batch 275 Discriminator Loss: 1.8139... Generator Loss: 0.5511
Epoch 1/1... Batch 300 Discriminator Loss: 1.3737... Generator Loss: 0.5604
Epoch 1/1... Batch 325 Discriminator Loss: 1.2862... Generator Loss: 0.7649
Epoch 1/1... Batch 350 Discriminator Loss: 1.6541... Generator Loss: 0.6317
Epoch 1/1... Batch 375 Discriminator Loss: 1.3316... Generator Loss: 0.6248
Epoch 1/1... Batch 400 Discriminator Loss: 1.3045... Generator Loss: 1.0211
Epoch 1/1... Batch 425 Discriminator Loss: 1.4099... Generator Loss: 0.7776
Epoch 1/1... Batch 450 Discriminator Loss: 1.4580... Generator Loss: 0.5204
Epoch 1/1... Batch 475 Discriminator Loss: 1.3382... Generator Loss: 0.9420
Epoch 1/1... Batch 500 Discriminator Loss: 1.2389... Generator Loss: 0.7913
Epoch 1/1... Batch 525 Discriminator Loss: 1.7159... Generator Loss: 1.1361
Epoch 1/1... Batch 550 Discriminator Loss: 1.2683... Generator Loss: 0.6614
Epoch 1/1... Batch 575 Discriminator Loss: 1.4605... Generator Loss: 0.6985
Epoch 1/1... Batch 600 Discriminator Loss: 1.3126... Generator Loss: 1.0289
Epoch 1/1... Batch 625 Discriminator Loss: 1.5364... Generator Loss: 0.5681
Epoch 1/1... Batch 650 Discriminator Loss: 1.2518... Generator Loss: 0.8893
Epoch 1/1... Batch 675 Discriminator Loss: 1.5014... Generator Loss: 0.9500
Epoch 1/1... Batch 700 Discriminator Loss: 1.4128... Generator Loss: 0.6104
Epoch 1/1... Batch 725 Discriminator Loss: 1.4558... Generator Loss: 0.6040
Epoch 1/1... Batch 750 Discriminator Loss: 1.3587... Generator Loss: 0.7164
Epoch 1/1... Batch 775 Discriminator Loss: 1.3624... Generator Loss: 0.7338
Epoch 1/1... Batch 800 Discriminator Loss: 1.4828... Generator Loss: 0.6943
Epoch 1/1... Batch 825 Discriminator Loss: 1.4322... Generator Loss: 0.6781
Epoch 1/1... Batch 850 Discriminator Loss: 1.2915... Generator Loss: 0.6345
Epoch 1/1... Batch 875 Discriminator Loss: 1.4098... Generator Loss: 0.6900
Epoch 1/1... Batch 900 Discriminator Loss: 1.4218... Generator Loss: 0.7665
Epoch 1/1... Batch 925 Discriminator Loss: 1.3756... Generator Loss: 0.8862
Epoch 1/1... Batch 950 Discriminator Loss: 1.4804... Generator Loss: 0.5767
Epoch 1/1... Batch 975 Discriminator Loss: 1.5218... Generator Loss: 1.2567
Epoch 1/1... Batch 1000 Discriminator Loss: 1.3360... Generator Loss: 0.7047
Epoch 1/1... Batch 1025 Discriminator Loss: 1.2826... Generator Loss: 0.8113
Epoch 1/1... Batch 1050 Discriminator Loss: 1.1960... Generator Loss: 0.7024
Epoch 1/1... Batch 1075 Discriminator Loss: 1.4356... Generator Loss: 0.6072
Epoch 1/1... Batch 1100 Discriminator Loss: 1.4540... Generator Loss: 0.8379
Epoch 1/1... Batch 1125 Discriminator Loss: 1.3395... Generator Loss: 0.7098
Epoch 1/1... Batch 1150 Discriminator Loss: 1.4576... Generator Loss: 1.0123
Epoch 1/1... Batch 1175 Discriminator Loss: 1.3661... Generator Loss: 0.6048
Epoch 1/1... Batch 1200 Discriminator Loss: 1.4454... Generator Loss: 0.7535
Epoch 1/1... Batch 1225 Discriminator Loss: 1.2599... Generator Loss: 0.7479
Epoch 1/1... Batch 1250 Discriminator Loss: 1.3333... Generator Loss: 0.7576
Epoch 1/1... Batch 1275 Discriminator Loss: 1.1170... Generator Loss: 0.7170
Epoch 1/1... Batch 1300 Discriminator Loss: 1.5303... Generator Loss: 0.5889
Epoch 1/1... Batch 1325 Discriminator Loss: 1.2349... Generator Loss: 0.8056
Epoch 1/1... Batch 1350 Discriminator Loss: 1.2173... Generator Loss: 1.0045
Epoch 1/1... Batch 1375 Discriminator Loss: 1.3512... Generator Loss: 0.8527
Epoch 1/1... Batch 1400 Discriminator Loss: 1.0503... Generator Loss: 0.7928
Epoch 1/1... Batch 1425 Discriminator Loss: 1.3864... Generator Loss: 0.6003
Epoch 1/1... Batch 1450 Discriminator Loss: 1.3281... Generator Loss: 0.5432
Epoch 1/1... Batch 1475 Discriminator Loss: 1.3961... Generator Loss: 0.5644
Epoch 1/1... Batch 1500 Discriminator Loss: 1.4121... Generator Loss: 0.6304
Epoch 1/1... Batch 1525 Discriminator Loss: 1.4601... Generator Loss: 0.4473
Epoch 1/1... Batch 1550 Discriminator Loss: 1.4331... Generator Loss: 0.6410
Epoch 1/1... Batch 1575 Discriminator Loss: 1.4572... Generator Loss: 0.5834
Epoch 1/1... Batch 1600 Discriminator Loss: 1.2964... Generator Loss: 0.7234
Epoch 1/1... Batch 1625 Discriminator Loss: 1.0743... Generator Loss: 0.9849
Epoch 1/1... Batch 1650 Discriminator Loss: 1.4004... Generator Loss: 0.7519
Epoch 1/1... Batch 1675 Discriminator Loss: 1.3991... Generator Loss: 0.7329
Epoch 1/1... Batch 1700 Discriminator Loss: 1.1647... Generator Loss: 0.9566
Epoch 1/1... Batch 1725 Discriminator Loss: 1.2903... Generator Loss: 0.6576
Epoch 1/1... Batch 1750 Discriminator Loss: 1.3193... Generator Loss: 0.5858
Epoch 1/1... Batch 1775 Discriminator Loss: 1.5439... Generator Loss: 0.5078
Epoch 1/1... Batch 1800 Discriminator Loss: 1.5792... Generator Loss: 0.7448
Epoch 1/1... Batch 1825 Discriminator Loss: 1.3801... Generator Loss: 0.6322
Epoch 1/1... Batch 1850 Discriminator Loss: 1.2843... Generator Loss: 0.6897
Epoch 1/1... Batch 1875 Discriminator Loss: 1.3407... Generator Loss: 0.7832
Epoch 1/1... Batch 1900 Discriminator Loss: 1.1732... Generator Loss: 0.8564
Epoch 1/1... Batch 1925 Discriminator Loss: 1.1887... Generator Loss: 0.6835
Epoch 1/1... Batch 1950 Discriminator Loss: 1.5677... Generator Loss: 0.4354
Epoch 1/1... Batch 1975 Discriminator Loss: 1.3940... Generator Loss: 0.6718
Epoch 1/1... Batch 2000 Discriminator Loss: 1.3759... Generator Loss: 0.6314
Epoch 1/1... Batch 2025 Discriminator Loss: 2.1194... Generator Loss: 1.5259
Epoch 1/1... Batch 2050 Discriminator Loss: 1.3537... Generator Loss: 0.6371
Epoch 1/1... Batch 2075 Discriminator Loss: 1.2992... Generator Loss: 1.0952
Epoch 1/1... Batch 2100 Discriminator Loss: 1.3192... Generator Loss: 0.5329
Epoch 1/1... Batch 2125 Discriminator Loss: 1.5271... Generator Loss: 0.5267
Epoch 1/1... Batch 2150 Discriminator Loss: 1.4751... Generator Loss: 0.6022
Epoch 1/1... Batch 2175 Discriminator Loss: 1.3982... Generator Loss: 0.5429
Epoch 1/1... Batch 2200 Discriminator Loss: 1.4135... Generator Loss: 0.5815
Epoch 1/1... Batch 2225 Discriminator Loss: 1.2652... Generator Loss: 0.7659
Epoch 1/1... Batch 2250 Discriminator Loss: 1.3463... Generator Loss: 0.6475
Epoch 1/1... Batch 2275 Discriminator Loss: 1.4952... Generator Loss: 0.4401
Epoch 1/1... Batch 2300 Discriminator Loss: 1.4090... Generator Loss: 0.7361
Epoch 1/1... Batch 2325 Discriminator Loss: 1.6370... Generator Loss: 0.3712
Epoch 1/1... Batch 2350 Discriminator Loss: 1.4472... Generator Loss: 0.5356
Epoch 1/1... Batch 2375 Discriminator Loss: 1.3870... Generator Loss: 0.5726
Epoch 1/1... Batch 2400 Discriminator Loss: 1.5956... Generator Loss: 0.4003
Epoch 1/1... Batch 2425 Discriminator Loss: 1.3127... Generator Loss: 0.8612
Epoch 1/1... Batch 2450 Discriminator Loss: 1.3177... Generator Loss: 0.6488
Epoch 1/1... Batch 2475 Discriminator Loss: 1.5268... Generator Loss: 0.5327
Epoch 1/1... Batch 2500 Discriminator Loss: 1.4182... Generator Loss: 0.5533
Epoch 1/1... Batch 2525 Discriminator Loss: 1.4396... Generator Loss: 0.4962
Epoch 1/1... Batch 2550 Discriminator Loss: 1.4264... Generator Loss: 0.5587
Epoch 1/1... Batch 2575 Discriminator Loss: 1.4497... Generator Loss: 0.7491
Epoch 1/1... Batch 2600 Discriminator Loss: 1.3476... Generator Loss: 0.6204
Epoch 1/1... Batch 2625 Discriminator Loss: 1.2666... Generator Loss: 0.6773
Epoch 1/1... Batch 2650 Discriminator Loss: 1.3154... Generator Loss: 0.9500
Epoch 1/1... Batch 2675 Discriminator Loss: 1.3542... Generator Loss: 0.6759
Epoch 1/1... Batch 2700 Discriminator Loss: 1.3989... Generator Loss: 0.6370
Epoch 1/1... Batch 2725 Discriminator Loss: 1.5427... Generator Loss: 0.3963
Epoch 1/1... Batch 2750 Discriminator Loss: 1.4153... Generator Loss: 0.8573
Epoch 1/1... Batch 2775 Discriminator Loss: 1.3948... Generator Loss: 0.5132
Epoch 1/1... Batch 2800 Discriminator Loss: 1.3878... Generator Loss: 0.6571
Epoch 1/1... Batch 2825 Discriminator Loss: 1.2909... Generator Loss: 0.7930
Epoch 1/1... Batch 2850 Discriminator Loss: 1.2913... Generator Loss: 0.7513
Epoch 1/1... Batch 2875 Discriminator Loss: 1.3833... Generator Loss: 0.6971
Epoch 1/1... Batch 2900 Discriminator Loss: 1.8215... Generator Loss: 0.2711
Epoch 1/1... Batch 2925 Discriminator Loss: 1.3102... Generator Loss: 0.7870
Epoch 1/1... Batch 2950 Discriminator Loss: 1.3812... Generator Loss: 0.8689
Epoch 1/1... Batch 2975 Discriminator Loss: 1.2841... Generator Loss: 0.7221
Epoch 1/1... Batch 3000 Discriminator Loss: 1.3006... Generator Loss: 0.6258
Epoch 1/1... Batch 3025 Discriminator Loss: 1.2564... Generator Loss: 0.8085
Epoch 1/1... Batch 3050 Discriminator Loss: 1.3054... Generator Loss: 0.8267
Epoch 1/1... Batch 3075 Discriminator Loss: 1.4513... Generator Loss: 0.5145
Epoch 1/1... Batch 3100 Discriminator Loss: 1.5024... Generator Loss: 0.5178
Epoch 1/1... Batch 3125 Discriminator Loss: 1.2771... Generator Loss: 0.6290
Epoch 1/1... Batch 3150 Discriminator Loss: 1.4904... Generator Loss: 0.5337
Epoch 1/1... Batch 3175 Discriminator Loss: 1.7473... Generator Loss: 0.2822
Epoch 1/1... Batch 3200 Discriminator Loss: 1.2814... Generator Loss: 0.5818
Epoch 1/1... Batch 3225 Discriminator Loss: 1.2667... Generator Loss: 0.8507
Epoch 1/1... Batch 3250 Discriminator Loss: 1.3461... Generator Loss: 0.7048
Epoch 1/1... Batch 3275 Discriminator Loss: 1.3908... Generator Loss: 0.6810
Epoch 1/1... Batch 3300 Discriminator Loss: 1.5962... Generator Loss: 0.4896
Epoch 1/1... Batch 3325 Discriminator Loss: 1.3883... Generator Loss: 0.6984
Epoch 1/1... Batch 3350 Discriminator Loss: 1.3403... Generator Loss: 0.5617
Epoch 1/1... Batch 3375 Discriminator Loss: 1.4238... Generator Loss: 0.5909
Epoch 1/1... Batch 3400 Discriminator Loss: 1.4471... Generator Loss: 0.6991
Epoch 1/1... Batch 3425 Discriminator Loss: 1.2480... Generator Loss: 0.7360
Epoch 1/1... Batch 3450 Discriminator Loss: 1.4895... Generator Loss: 0.4793
Epoch 1/1... Batch 3475 Discriminator Loss: 1.4775... Generator Loss: 0.8415
Epoch 1/1... Batch 3500 Discriminator Loss: 1.7970... Generator Loss: 1.8135
Epoch 1/1... Batch 3525 Discriminator Loss: 1.4354... Generator Loss: 0.5326
Epoch 1/1... Batch 3550 Discriminator Loss: 1.4255... Generator Loss: 0.6740
Epoch 1/1... Batch 3575 Discriminator Loss: 1.4306... Generator Loss: 0.6926
Epoch 1/1... Batch 3600 Discriminator Loss: 1.3646... Generator Loss: 0.6242
Epoch 1/1... Batch 3625 Discriminator Loss: 1.4339... Generator Loss: 0.5786
Epoch 1/1... Batch 3650 Discriminator Loss: 1.4726... Generator Loss: 0.7077
Epoch 1/1... Batch 3675 Discriminator Loss: 1.3457... Generator Loss: 0.6954
Epoch 1/1... Batch 3700 Discriminator Loss: 1.3383... Generator Loss: 0.7786
Epoch 1/1... Batch 3725 Discriminator Loss: 1.4007... Generator Loss: 0.5780
Epoch 1/1... Batch 3750 Discriminator Loss: 1.2227... Generator Loss: 0.6316
Epoch 1/1... Batch 3775 Discriminator Loss: 1.3678... Generator Loss: 0.6253
Epoch 1/1... Batch 3800 Discriminator Loss: 1.6792... Generator Loss: 0.3014
Epoch 1/1... Batch 3825 Discriminator Loss: 1.3606... Generator Loss: 0.5616
Epoch 1/1... Batch 3850 Discriminator Loss: 1.4370... Generator Loss: 0.5627
Epoch 1/1... Batch 3875 Discriminator Loss: 1.4757... Generator Loss: 0.6162
Epoch 1/1... Batch 3900 Discriminator Loss: 1.3802... Generator Loss: 0.5880
Epoch 1/1... Batch 3925 Discriminator Loss: 1.3596... Generator Loss: 0.6841
Epoch 1/1... Batch 3950 Discriminator Loss: 1.3589... Generator Loss: 0.7714
Epoch 1/1... Batch 3975 Discriminator Loss: 1.3919... Generator Loss: 0.9991
Epoch 1/1... Batch 4000 Discriminator Loss: 1.3948... Generator Loss: 0.7045
Epoch 1/1... Batch 4025 Discriminator Loss: 1.4799... Generator Loss: 0.7121
Epoch 1/1... Batch 4050 Discriminator Loss: 1.2970... Generator Loss: 0.6965
Epoch 1/1... Batch 4075 Discriminator Loss: 1.2495... Generator Loss: 0.6954
Epoch 1/1... Batch 4100 Discriminator Loss: 1.4336... Generator Loss: 0.4888
Epoch 1/1... Batch 4125 Discriminator Loss: 1.3140... Generator Loss: 0.5975
Epoch 1/1... Batch 4150 Discriminator Loss: 1.6906... Generator Loss: 0.3037
Epoch 1/1... Batch 4175 Discriminator Loss: 1.4021... Generator Loss: 0.6381
Epoch 1/1... Batch 4200 Discriminator Loss: 1.2784... Generator Loss: 0.5606
Epoch 1/1... Batch 4225 Discriminator Loss: 1.4328... Generator Loss: 0.6392
Epoch 1/1... Batch 4250 Discriminator Loss: 1.3278... Generator Loss: 0.6164
Epoch 1/1... Batch 4275 Discriminator Loss: 1.4672... Generator Loss: 0.4824
Epoch 1/1... Batch 4300 Discriminator Loss: 1.2545... Generator Loss: 0.8038
Epoch 1/1... Batch 4325 Discriminator Loss: 1.3870... Generator Loss: 0.5915
Epoch 1/1... Batch 4350 Discriminator Loss: 1.3809... Generator Loss: 0.5698
Epoch 1/1... Batch 4375 Discriminator Loss: 1.4176... Generator Loss: 0.4317
Epoch 1/1... Batch 4400 Discriminator Loss: 1.3320... Generator Loss: 0.7083
Epoch 1/1... Batch 4425 Discriminator Loss: 1.2900... Generator Loss: 0.7923
Epoch 1/1... Batch 4450 Discriminator Loss: 1.2431... Generator Loss: 0.7587
Epoch 1/1... Batch 4475 Discriminator Loss: 1.6692... Generator Loss: 0.3319
Epoch 1/1... Batch 4500 Discriminator Loss: 1.3584... Generator Loss: 0.5825
Epoch 1/1... Batch 4525 Discriminator Loss: 1.2002... Generator Loss: 0.8491
Epoch 1/1... Batch 4550 Discriminator Loss: 1.4206... Generator Loss: 0.5546
Epoch 1/1... Batch 4575 Discriminator Loss: 1.4437... Generator Loss: 1.0852
Epoch 1/1... Batch 4600 Discriminator Loss: 1.3471... Generator Loss: 0.6035
Epoch 1/1... Batch 4625 Discriminator Loss: 1.2960... Generator Loss: 0.9465
Epoch 1/1... Batch 4650 Discriminator Loss: 1.5359... Generator Loss: 0.7542
Epoch 1/1... Batch 4675 Discriminator Loss: 1.3560... Generator Loss: 0.6479
Epoch 1/1... Batch 4700 Discriminator Loss: 1.4988... Generator Loss: 1.0885
Epoch 1/1... Batch 4725 Discriminator Loss: 1.2194... Generator Loss: 0.6813
Epoch 1/1... Batch 4750 Discriminator Loss: 1.4139... Generator Loss: 0.4898
Epoch 1/1... Batch 4775 Discriminator Loss: 1.3368... Generator Loss: 0.6979
Epoch 1/1... Batch 4800 Discriminator Loss: 1.4962... Generator Loss: 0.9058
Epoch 1/1... Batch 4825 Discriminator Loss: 1.3492... Generator Loss: 0.6623
Epoch 1/1... Batch 4850 Discriminator Loss: 1.3349... Generator Loss: 0.5618
Epoch 1/1... Batch 4875 Discriminator Loss: 1.3616... Generator Loss: 0.7208
Epoch 1/1... Batch 4900 Discriminator Loss: 1.3905... Generator Loss: 0.7194
Epoch 1/1... Batch 4925 Discriminator Loss: 1.2747... Generator Loss: 0.6423
Epoch 1/1... Batch 4950 Discriminator Loss: 1.4670... Generator Loss: 0.5780
Epoch 1/1... Batch 4975 Discriminator Loss: 1.3794... Generator Loss: 0.5254
Epoch 1/1... Batch 5000 Discriminator Loss: 1.3985... Generator Loss: 0.4944
Epoch 1/1... Batch 5025 Discriminator Loss: 1.2865... Generator Loss: 0.7886
Epoch 1/1... Batch 5050 Discriminator Loss: 1.4320... Generator Loss: 0.8041
Epoch 1/1... Batch 5075 Discriminator Loss: 1.3686... Generator Loss: 0.5329
Epoch 1/1... Batch 5100 Discriminator Loss: 1.5166... Generator Loss: 0.5439
Epoch 1/1... Batch 5125 Discriminator Loss: 1.2534... Generator Loss: 0.8450
Epoch 1/1... Batch 5150 Discriminator Loss: 1.1693... Generator Loss: 0.8247
Epoch 1/1... Batch 5175 Discriminator Loss: 2.0067... Generator Loss: 1.9128
Epoch 1/1... Batch 5200 Discriminator Loss: 1.2236... Generator Loss: 0.8708
Epoch 1/1... Batch 5225 Discriminator Loss: 1.5597... Generator Loss: 0.4229
Epoch 1/1... Batch 5250 Discriminator Loss: 1.3921... Generator Loss: 0.5136
Epoch 1/1... Batch 5275 Discriminator Loss: 1.3359... Generator Loss: 0.5657
Epoch 1/1... Batch 5300 Discriminator Loss: 1.2721... Generator Loss: 0.6992
Epoch 1/1... Batch 5325 Discriminator Loss: 1.4154... Generator Loss: 0.7087
Epoch 1/1... Batch 5350 Discriminator Loss: 1.2814... Generator Loss: 0.6506
Epoch 1/1... Batch 5375 Discriminator Loss: 1.4165... Generator Loss: 0.5714
Epoch 1/1... Batch 5400 Discriminator Loss: 1.5342... Generator Loss: 0.3950
Epoch 1/1... Batch 5425 Discriminator Loss: 1.3713... Generator Loss: 0.5776
Epoch 1/1... Batch 5450 Discriminator Loss: 1.3209... Generator Loss: 0.7001
Epoch 1/1... Batch 5475 Discriminator Loss: 1.7594... Generator Loss: 0.9630
Epoch 1/1... Batch 5500 Discriminator Loss: 1.5125... Generator Loss: 0.4528
Epoch 1/1... Batch 5525 Discriminator Loss: 1.3591... Generator Loss: 0.6426
Epoch 1/1... Batch 5550 Discriminator Loss: 1.4493... Generator Loss: 0.5649
Epoch 1/1... Batch 5575 Discriminator Loss: 1.2977... Generator Loss: 0.6390
Epoch 1/1... Batch 5600 Discriminator Loss: 1.3984... Generator Loss: 0.5819
Epoch 1/1... Batch 5625 Discriminator Loss: 1.8748... Generator Loss: 1.9879
Epoch 1/1... Batch 5650 Discriminator Loss: 1.2892... Generator Loss: 0.7422
Epoch 1/1... Batch 5675 Discriminator Loss: 1.2809... Generator Loss: 1.2893
Epoch 1/1... Batch 5700 Discriminator Loss: 1.4332... Generator Loss: 1.0353
Epoch 1/1... Batch 5725 Discriminator Loss: 1.2766... Generator Loss: 0.6061
Epoch 1/1... Batch 5750 Discriminator Loss: 1.4687... Generator Loss: 0.4274
Epoch 1/1... Batch 5775 Discriminator Loss: 1.4849... Generator Loss: 0.4200
Epoch 1/1... Batch 5800 Discriminator Loss: 1.0958... Generator Loss: 1.6752
Epoch 1/1... Batch 5825 Discriminator Loss: 1.3896... Generator Loss: 0.8014
Epoch 1/1... Batch 5850 Discriminator Loss: 1.5281... Generator Loss: 0.4013
Epoch 1/1... Batch 5875 Discriminator Loss: 1.2380... Generator Loss: 0.9023
Epoch 1/1... Batch 5900 Discriminator Loss: 1.3913... Generator Loss: 0.4706
Epoch 1/1... Batch 5925 Discriminator Loss: 1.5354... Generator Loss: 0.3871
Epoch 1/1... Batch 5950 Discriminator Loss: 1.4358... Generator Loss: 1.0589
Epoch 1/1... Batch 5975 Discriminator Loss: 1.3545... Generator Loss: 0.6395
Epoch 1/1... Batch 6000 Discriminator Loss: 1.3052... Generator Loss: 0.5618
Epoch 1/1... Batch 6025 Discriminator Loss: 1.1767... Generator Loss: 0.7316
Epoch 1/1... Batch 6050 Discriminator Loss: 1.3444... Generator Loss: 0.5300
Epoch 1/1... Batch 6075 Discriminator Loss: 1.2373... Generator Loss: 0.6791
Epoch 1/1... Batch 6100 Discriminator Loss: 1.2305... Generator Loss: 0.6362
Epoch 1/1... Batch 6125 Discriminator Loss: 1.3708... Generator Loss: 0.4917
Epoch 1/1... Batch 6150 Discriminator Loss: 1.3100... Generator Loss: 0.5825
Epoch 1/1... Batch 6175 Discriminator Loss: 1.3774... Generator Loss: 0.4890
Epoch 1/1... Batch 6200 Discriminator Loss: 1.2477... Generator Loss: 0.7341
Epoch 1/1... Batch 6225 Discriminator Loss: 1.4748... Generator Loss: 1.0539
Epoch 1/1... Batch 6250 Discriminator Loss: 1.3815... Generator Loss: 0.7939
Epoch 1/1... Batch 6275 Discriminator Loss: 1.4291... Generator Loss: 0.4262
Epoch 1/1... Batch 6300 Discriminator Loss: 1.3511... Generator Loss: 0.6128
Epoch 1/1... Batch 6325 Discriminator Loss: 1.4776... Generator Loss: 1.9493

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.